Software Reliability Modeling Incorporating Log-Logistic Testing-Effort with Imperfect Debugging

2010 ◽  
Author(s):  
N. Ahmad ◽  
M. G. M. Khan ◽  
L. S. Rafi ◽  
Swapan Paruya ◽  
Samarjit Kar ◽  
...  
Keyword(s):  
Software Reliability ◽  
Reliability Modeling ◽  
Imperfect Debugging ◽  
Testing Effort

Toward a Unified Approach to Software Reliability Modeling under Imperfect Debugging

2015 ◽  
Vol 764-765 ◽  
pp. 979-982
Author(s):  
Jung Hua Lo
Keyword(s):  
Software Reliability ◽  
Growth Models ◽  
Failure Detection ◽  
Mean Value ◽  
Unified Approach ◽  
Development Environment ◽  
Reliability Modeling ◽  
Imperfect Debugging ◽  
Correction Process ◽  
Function Number

Many software reliability growth models (SRGMs) have been developed to estimate some useful measures such as the mean value function, number of remaining faults, and failure detection rate. Most of these models have focused on the failure detection process and not given equal priority to modeling the fault correction process. But, most latent software errors may remain uncorrected for a long time even after they are detected, which increases their impact. The remaining software faults are often one of the most unreliable reasons for software quality. Therefore, we develop a general framework of the modeling of the failure detection and fault correction processes. Furthermore, it is assumed that a detected fault is immediately removed and is perfectly repaired with no new faults being introduced for the traditional SRGMs. In reality, it is impossible to remove all faults from the fault correction process and have a fault-free effect on the software development environment. In order to relax this perfect debugging assumption, we introduce the possibility of imperfect debugging phenomenon. Finally, numerical examples are shown to illustrate the results of the unified approach for integration of the detection and correction process under imperfect debugging.

Markovian Multiple Change-Point Modeling for Software Reliability Assessment

2020 ◽  
Vol 27 (06) ◽  
pp. 2050017
Author(s):  
Shinji Inoue ◽  
Shigeru Yamada
Keyword(s):  
Software Reliability ◽  
Change Point ◽  
Reliability Assessment ◽  
Testing Time ◽  
Modeling Framework ◽  
Reliability Modeling ◽  
Imperfect Debugging ◽  
Testing Environment ◽  
Software Failure ◽  
Testing Phase

We discuss software reliability modeling reflecting actual situation in a testing phase based on a Markovian software reliability modeling framework. Concretely, we discuss Markovian imperfect debugging modeling for software reliability assessment with multiple changes of testing environment. Testing-time changing the testing environment is called change-point. Taking into account the effect of change-point in software reliability growth modeling is expected to improve the accuracy of software reliability assessment because it is often observed that the stochastic characteristic of software failure-occurrence or fault-detection phenomenon is changed in an actual testing phase. Numerical examples for software reliability assessment based on our proposed approach are also shown by using actual software failure-occurrence time data. Further, we discuss the usefulness of considering the effect of the imperfect debugging and the multiple change-point into software reliability modeling by comparing the estimated behavior of the mean time between software failures based on our model and the existing related models.

An Assessment of Incorporating Log-Logistic Testing Effort Into Imperfect Debugging Delayed S-Shaped Software Reliability Growth Model

2021 ◽  
Vol 9 (3) ◽  
pp. 23-41
Author(s):  
Nesar Ahmad ◽  
Aijaz Ahmad ◽  
Sheikh Umar Farooq
Keyword(s):  
Software Reliability ◽  
Growth Models ◽  
Least Square ◽  
Model Parameters ◽  
Reliability Growth ◽  
Imperfect Debugging ◽  
Testing Effort ◽  
Software Reliability Growth Models ◽  
Software Reliability Growth ◽  
Effort Function

Software reliability growth models (SRGM) are employed to aid us in predicting and estimating reliability in the software development process. Many SRGM proposed in the past claim to be effective over previous models. While some earlier research had raised concern regarding use of delayed S-shaped SRGM, researchers later indicated that the model performs well when appropriate testing-effort function (TEF) is used. This paper proposes and evaluates an approach to incorporate the log-logistic (LL) testing-effort function into delayed S-shaped SRGMs with imperfect debugging based on non-homogeneous Poisson process (NHPP). The model parameters are estimated by weighted least square estimation (WLSE) and maximum likelihood estimation (MLE) methods. The experimental results obtained after applying the model on real data sets and statistical methods for analysis are presented. The results obtained suggest that performance of the proposed model is better than the other existing models. The authors can conclude that the log-logistic TEF is appropriate for incorporating into delayed S-shaped software reliability growth models.

Sign in / Sign up

Export Citation Format

Share Document